While the main application of resistive switching structures currently targets non-volatile memories, increasing interest is being focused on their logic applications. Resistive switches are suitable for Boolean logic, neuromorphic computing and for implementation of Zadeh fuzzy logic. In this work, we analyzed implementation of the resistive switching structures for logic application based on Zadeh fuzzy logic. Resistive switching structures based on hafnium oxide and tantalum oxide were connected in an anti-serial configuration (complementary resistive switch). The complementary resistive switches integrated into logic circuit for Min-Max function implementation were analyzed using quasi-static voltage sweeps. We have shown that the accuracy of the Min/Max function determination depends on the ratio of the high and low resistivity states of the single switches. Determination of the Min/Max values is relevant only above the threshold voltage of the resistive structures. Reproducibility of the Min/Max function constructed from the resistive switching structures was evaluated. In addition, pulsed reconfiguration of complementary resistive switch using 100 ns long pulses was demonstrated.

One-hot encoding is the prevalent method used in neural networks to represent multi-class categorical data. Its success stems from its ease of use and interpretability as a probability distribution when accompanied by a softmax activation function. However, one-hot encoding leads to very high dimensional vector representations when the categorical data’s cardinality is high. The Hamming distance in one-hot encoding is equal to two from the coding theory perspective, which does not allow detection or error-correcting capabilities. Binary coding provides more possibilities for encoding categorical data into the output codes, which mitigates the limitations of the one-hot encoding mentioned above. We propose a novel method based on Zadeh fuzzy logic to train binary output codes holistically. We study linear block codes for their possibility of separating class information from the checksum part of the codeword, showing their ability not only to detect recognition errors by calculating non-zero syndrome, but also to evaluate the truth-value of the decision. Experimental results show that the proposed approach achieves similar results as one-hot encoding with a softmax function in terms of accuracy, reliability, and out-of-distribution performance. It suggests a good foundation for future applications, mainly classification tasks with a high number of classes.

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